Identification of objects in a 3D video using non/over reflective clothing

ABSTRACT

A method includes generating a depth map from at least one image, detecting objects in the depth map, and identifying anomalies in the objects from the depth map. Another method includes identifying at least one anomaly in an object in a depth map, and using the anomaly to identify future occurrences of the object. A system includes a three dimensional (3D) imaging system to generate a depth map from at least one image, an object detector to detect objects within the depth map, and an anomaly detector to detect anomalies in the detected objects, wherein the anomalies are logical gaps and/or logical protrusions in the depth map.

FIELD OF THE INVENTION

The present invention relates to three dimensional (3D) imaginggenerally and to depth maps in particular.

BACKGROUND OF THE INVENTION

3D imaging is known in the art. Several techniques are commonly used tocreate such images. U.S. Pat. No. 6,091,905, and U.S. Pat. No.6,100,517, both assigned to the common assignees of the presentinvention and incorporated herein by reference, disclose methods andsystems for rapidly and easily determining the distance of variouspoints in a scene. The disclosed methods and systems detect reflectedradiation, such as infrared (IR) or near infrared (NIR) radiation, tocreate a depth map. It will be appreciated that further references inthe specification to IR may be exemplary; NIR and/or other types ofradiation may also be used.

FIGS. 1 and 2, to which reference is now made, illustrate a typical suchsystem and its exemplary output. As shown in FIG. 1A, system 100comprises an IR generator 10, an IR detector 20 and a compensator 30.FIG. 2 shows exemplary images produced by system 100.

In a typical implementation, IR generator 10 generates IR radiation 15and directs it at a scene, including, for example, object 40. IRdetector 20 detects the intensity of radiation 25 as reflected fromobject 40. In general, the greater the intensity, the closer the object.FIG. 2 shows an exemplary RGB image 41A of object 40 and matching IRimages 45A.

The intensity of radiation 25 detected by IR detector 20 is a functionof both the distance to object 40 and its reflective properties.Reflectivity is the fraction of incident radiation reflected by asurface. Some materials, such as glass or polished metal are highlyreflective. Other materials, such as matt paint, have lowerreflectivity. Therefore, the material of an object can affect theintensity of the images received by IR detector 20.

To compensate for different reflective properties, as disclosed in U.S.Pat. Nos. 6,091,905 and 6,100,517, IR radiation 15 comprises analternating series of continuous and pulsed radiations. The resultingseries of IR images 45 are forwarded to compensator 30 which processesthem to compensate for the different reflective properties. Compensator30 typically divides a grayscale value for the intensity of a pixelduring the continuous radiation period by a grayscale value for the samepixel during the period of pulsed radiation, with the quotient betweenthe two being inversely proportional to a calculated value for depth,i.e. D=P/C, where D represents depth, P represents the pixel intensityreceived during pulsed radiation, and C represents the pixel intensityreceived during continuous radiation. The higher the value for depth,the closer the object. Compensator 30 produces a series of depth maps 50based on the input IR images 45. FIG. 2 shows an exemplary depth map 50Acorresponding to IR images 45A.

SUMMARY OF THE PRESENT INVENTION

An object of the present invention is to improve upon the prior art.

There is therefore provided, in accordance with a preferred embodimentof the present invention, a method including generating a depth map fromat least one image, detecting objects in the depth map, and identifyinganomalies in the objects from the depth map.

Moreover, in accordance with a preferred embodiment of the presentinvention, the identifying includes calculating a depth difference bycomparing depths of at least two regions in the objects, and determiningthat an anomaly exists where an absolute value of the depth differenceexceeds a threshold.

Further, in accordance with a preferred embodiment of the presentinvention, the at least one image is a first image generated usingcontinuous radiation and a second image of the same scene using pulsedradiation, and the identifying includes defining a depth threshold and apixel intensity threshold, finding associated pixels of the first andsecond images which have intensities below a threshold in both images,and determining that an anomaly exists where a calculated differencebetween the intensity of the associated pixels is less than the pixelintensity threshold and a derived depth exceeds the depth threshold.

Further, in accordance with a preferred embodiment of the presentinvention, the anomalies are logical gaps in the objects whose pixeldepths are less than those of pixels in at least one adjacent region ofthe object.

Additionally, in accordance with a preferred embodiment of the presentinvention, the anomalies are logical protrusions in the objects whosepixel depths are greater than those of pixels in at least one adjacentregion of the object.

Moreover, in accordance with a preferred embodiment of the presentinvention, the method also includes compensating for the anomalies byreplacing pixels associated with the anomalies with pixels of depthsimilar to that of at least one region adjacent to the anomaly, wherethe region is a part of the object.

Further, in accordance with a preferred embodiment of the presentinvention, the method also includes marking the anomalies and using themarked anomalies to identify future occurrences of the detected objects.

There is also provided, in accordance with a preferred embodiment of thepresent invention, a method including identifying at least one anomalyin an object in a depth map and using the at least one anomaly toidentify future occurrences of the object.

Moreover, in accordance with a preferred embodiment of the presentinvention, the anomalies are logical gaps in the objects whose pixeldepths are less than those of pixels in at lease one adjacent region ofthe object.

Further, in accordance with a preferred embodiment of the presentinvention, the anomalies are caused by materials with lower reflectiveproperties than those of other materials represented in the depth map.

Still further, in accordance with a preferred embodiment of the presentinvention, the anomalies are logical protrusions in the objects whosepixel depths are greater than those of pixels in at least one adjacentregion of the object.

Additionally, in accordance with a preferred embodiment of the presentinvention, the anomalies are caused by materials with greater reflectiveproperties than those of other materials represented in the depth map.

Moreover, in accordance with a preferred embodiment of the presentinvention, the method also includes performing the identifying as partof a calibration process prior to operation.

Further, in accordance with a preferred embodiment of the presentinvention, the object is a part of a subject's body.

Still further, in accordance with a preferred embodiment of the presentinvention, the at least one anomaly is caused by a contrast inreflectivity between at least two parts of the subject's body.

Moreover, in accordance with a preferred embodiment of the presentinvention, the using includes distinguishing between said object and asecond similar object.

Further, in accordance with a preferred embodiment of the presentinvention, the object and the similar object are a pair of objects. Oneof the pair of objects is identified as a left object and one of thepair of objects is a right object.

Moreover, in accordance with a preferred embodiment of the presentinvention, the method also includes marking the object as a specificindividual.

Additionally, in accordance with a preferred embodiment of the presentinvention, the method also includes compensating for said anomalies byreplacing pixels associated with the anomalies with pixels of depthsimilar to that of at least one region adjacent to the anomaly, wherethe region is a part of the object.

There is also provided, in accordance with a preferred embodiment of thepresent invention, a system including a three dimensional (3D) imagingsystem to generate a depth map from at least one image, an objectdetector to detect objects within the depth map, and an anomaly detectorto detect anomalies in the detected objects, where the anomalies are atleast one of a logical gap and a logical protrusion in the depth map.

Moreover, in accordance with a preferred embodiment of the presentinvention, the 3D imaging system includes means to process imagesgenerated from both pulsed and continuous radiation, and the anomalydetector includes means to compare pixel intensities from associatedregions of the associated images to detect anomalies when a depth of thepixels is closer than a threshold.

Further, in accordance with a preferred embodiment of the presentinvention, the anomaly detector includes means to compare a differencein pixel depth between at least two regions of the detected objects.

Still further, in accordance with a preferred embodiment of the presentinvention, the system also includes an anomaly detector to generate amodified version of the depth map without the detected anomalies.

Additionally, in accordance with a preferred embodiment of the presentinvention, the system also includes an anomaly marker to mark thedetected anomalies and associate them with the detected objects.

Moreover, in accordance with a preferred embodiment of the presentinvention, the object detector also includes means to use the markedanomalies to detect and identify the associated objects.

Further, in accordance with a preferred embodiment of the presentinvention, the anomaly marker includes a unit to associate the detectedanomaly as representing a specific individual subject.

Still further, in accordance with a preferred embodiment of the presentinvention, the individual subject is a participant in a multiplayerapplication.

Moreover, in accordance with a preferred embodiment of the presentinvention, the object detector also includes a unit to use the markedanomalies to identify an individual subject in an application withmultiple participants.

There is also provided, in accordance with a preferred embodiment of thepresent invention, a method including analyzing reflective properties ofat least one object represented in a depth map, wherein the at least oneobject is at least one of a part of a subject's body and an object onthe subject's body, marking anomalies caused by differences in thereflective properties and identifying future occurrences of the at leastone object based on the marked anomalies.

Further, in accordance with a preferred embodiment of the presentinvention, the method also includes using the marked anomalies todistinguish between left and right paired objects, wherein the at leastone object is one of the paired objects.

Still further, in accordance with a preferred embodiment of the presentinvention, the method also includes using the marked anomalies todistinguish between players of a multiplayer game.

Finally, in accordance with a preferred embodiment of the presentinvention, the at least one object is clothing, a clothing accessory, apart of a subject's body, jewelry or a medical artifact.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 is a schematic illustration of the operation of a prior art,three-dimensional (3D) camera;

FIG. 2 is an illustration of exemplary output of elements of the 3Dcamera of FIG. 1;

FIG. 3 is an illustration of IR image and a depth map of an arm with awristwatch;

FIG. 4 is a schematic illustration of a novel system for detecting andcompensating for anomalies in depth maps, constructed and operative inaccordance with a preferred embodiment of the present invention;

FIG. 5 is an illustration of images based on the images of FIG. 3;

FIG. 6 is an illustration of images and depth maps of a scene with a manstanding with his arms placed one on top of the other in front of him;and

FIG. 7 is a schematic illustration of a novel system 300 for identifyingand tagging body parts in a 3D image, constructed and operative inaccordance with a preferred embodiment of the present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

It will be appreciated that system 100 may not always properlycompensate for the differing reflective properties of every object in ascene. Objects with generally different reflective properties may causeanomalies in the representation of depth in depth maps produced bysystem 100.

For example, a dark leather wristband may reflect relatively littleradiation in comparison with a person's arm and/or hand and compensator30 may not successfully handle this. FIG. 3, to which reference is nowmade, shows an exemplary IR image 45B of an arm with a wristwatch. Whenprocessed by compensator 30, there may be a logical “gap” 51 where thewristwatch should be located in the representation of depth map 50B.While the wristwatch area is still shown in depth map 50B, the resultinglower pixel intensity in gap 51 appears to indicate that the wrist isactually farther away than the hand or arm. Depending on the materialsused, it may even appear that the hand and arm are disconnected.

It will be appreciated that other types of material may result in othertypes of anomalies in the representation of depth map 50B. For example,a highly reflective material, such as a shiny wristwatch with a metallicband, may result in an artificial protrusion in depth 50B. Such awristwatch may be represented as being much closer than it actually is.

Such anomalies may be assumed to be persistent during a given series ofor session of depth maps. For example, if a subject may be wearing awristwatch at the beginning of a session, it may generally be expectedthat the subject will continue to wear the wristwatch on the same armfor the duration of the session. Accordingly, Applicants have realizedthat such anomalies may be used to “tag” an object once it has beendetected and identified. For example, a particular anomaly, such as onecaused by a wristwatch, may be identified as associated with a rightarm. Whenever the particular anomaly is observed it may therefore beassumed to be part of a right arm, without the need to establish and/orconfirm that assumption via other methods.

It may also be advantageous to compensate for such anomalies whengenerating and/or displaying depth maps. FIG. 4, to which reference isnow made, illustrates a novel system 200 for detecting and compensatingfor anomalies in depth maps. System 200 is designed and operative inaccordance with a preferred embodiment of the present invention and maycomprise 3D imaging system 100, an object detector 110, an anomalydetector 120 and an anomaly compensator 130.

Object detector 110 may receive depth maps 50 from imaging system 100and may process them to detect identifiable objects, such as, forexample, arms and legs.

It will be appreciated that object identifiers are known in the art.Accordingly, object identifier 110 may be implemented using acommercially available object identifier capable of identifying objectsfrom a 2D image, such as, for example, the HAAR classifier, as disclosedin the article Rapid Object Detection using a Boosted Cascade of SimpleFeatures, by Paul Viola and Michael Jones. Object identifier 110 mayalso be based on the Fujimura elliptical head tracker as disclosed in ARobust Elliptical Head Tracker, by Harsh Nanda and Kikuo Fujimura.

Object detector 110 may forward object identified images 51 to anomalydetector 120. Anomaly detector 120 may inspect the depth pixels of anidentified object to detect regions with relatively abrupt changes indepth. For the purposes of this inspection, a pixel depth difference maybe defined as the absolute value of the difference in depth betweenadjacent pixels or groups of pixels in the identified object. Athreshold may be defined for a reasonable pixel depth difference to beexpected from adjacent pixels or groups of pixels. It will beappreciated that by using an absolute value for the pixel depthdifference, the same threshold may be used to detect both anomalous gapsand protrusions.

In accordance with an exemplary embodiment of the present invention, an8-bit integer may be used to define a grayscale range to measure pixeldepth, with values between 0 and 255. An exemplary threshold may bedefined as a value of 50. Exceeding this threshold may indicate thatthere may be an anomaly in the object's representation caused by thedifferent reflective properties of the area in question.

FIG. 5, to which reference is now briefly made, shows images based onthe images of FIG. 3. IR image 45B and depth map 50B together illustratean exemplary arm object 151 as detected by object detector 110. 3D image50B′ represents a 3D rendering of arm object 151 in accordance with thedepth shown in depth map 50B. Arm object 151 may comprise an upper armarea 160, a wrist area 165 and a hand area 170. Wrist area 165 asdepicted in depth map 50B may represent pixels of a lesser depth causedby a leather wristwatch or bracelet. It will be appreciated that thelesser depth of the pixels in wrist area 165 may have an anomalouseffect on a 3D visualization of arm object 151. For example, as shown in3D image 50B′, it may appear that wrist area 165 may be recessed or evenfully disconnected from the rest of arm object 151.

Returning to FIG. 4, gap detector 120 may methodically inspect thepixels or groups of pixels in upper arm area 160 (FIG. 5A), repeatedlychecking whether or not a calculated pixel depth difference is lower orgreater than the defined threshold. When anomaly detector 120 beginsinspecting wrist area 165 it may detect that the calculated pixel depthdifference may exceed the defined threshold, thus indicating that ananomaly may be starting.

As anomaly detector 120 continues checking the pixels of wrist area 165,the pixel depth difference may fall below the defined threshold, thusindicating a continuation of the gap. When gap detector 120 may begininspecting pixels or groups of pixels in hand area 170, the depthdetected by anomaly detector 120 may once again exceed the definedthreshold, thus indicating an end of the anomaly. In such manner,anomaly detector 120 may methodically inspect all of the area includedin arm object 151 in order to fully map gap 265 in wrist area 165.

In accordance with an alternative embodiment of the present invention,anomalies may also be detected by analysis of individual pixels withoutcomparing them to other pixels or regions of pixels in an object. Asdisclosed in U.S. Pat. Nos. 6,091,905 and 6,100,517, compensator 30(FIG. 1) may divide the value of pixel intensity from continuousradiation by the value of pixel intensity from pulsed radiation toderive a compensated value for depth. It will be appreciated that, whenthe pixel intensity values are low, any noise is intensified by thedivision. This is particularly acute when the pixel values from the twotypes of radiation also have values close to each other, resulting indepth values close to the camera. These depth values may be anomalous.Thus, anomaly detector 120 may also use IR images 45 to detect anomaliesin an associated depth map 50. If the pixel intensities in the two inputimages 45 (from both pulsed and continuous radiation) are both low andsimilar to each other, anomaly detector 120 may indicate an anomaly inthe associated region of the derived depth map 50.

Returning to FIG. 5, gap identified depth map 52B represents anexemplary output by gap detector 120: a depth map of arm object 151 withan anomalous gap 265 marked in white. Gap compensator 130 (FIG. 4) mayreceive arm object 151 for processing.

Gap compensator 130 may use any suitable “inpainting” method to processgap 265. Inpainting methods may generally use the properties of aregion's boundaries to fill in gaps or repaint part or all of a region.An exemplary implementation of inpainting may be the “roifill” functionin Matlab, commercially available from The MathWorks in the UnitedStates. Roifill may fill in a specified polygon in an image and may beused on depth maps. It may smoothly interpolate inward from the pixelvalues on the boundary of the polygon by solving a discrete differentialequation.

In accordance with a preferred embodiment of the present invention,compensator 130 may perform a grayscale reconstruction as described inthe article “Morphological Grayscale Reconstruction in Image Analysis:Applications and Efficient Algorithms” by Luc Vincent (IEEE Transactionson Image Processing, Vol. 2, No. 2, April 1993, pp 176-201). Anomalycompensator 130 may identify a pixel with the highest depth within armobject 151. Anomaly compensator 130 may then employ the identified pixelto impose a global maximum depth in order to produce a reconstructedversion of arm object 151 as per the process as disclosed in theabovementioned article. Anomaly compensator 130 may fill-in gap 265 withpixels from a corresponding area in the reconstructed image.

Gap filled depth map 53B illustrates an exemplary correction of gapidentified image 52B. Gap 265 may be “filled in” and may generally blendin with the rest of arm object 151. It will be appreciated that theidentification of an anomalous gap is exemplary. System 200 may also beused to identify and compensate for anomalous protrusions as well.

Reference is now made to FIG. 6. RGB image 210 shows a scene with a manstanding with his arms placed one on top of the other in front of him.IR Images 220 and 225 represent the same scene. Depth map 230 may be theoutput of imaging system 200 generated by processing IR images 220 and225.

It will be appreciated that it may more difficult to differentiatebetween the left and right hands of the man in depth map 230 than inimages 220 and 225. It will further be appreciated that some 3Dapplications, for example interactive computer games, may require thecapability to differentiate between left and right hands. Accordingly itmay be advantageous to provide a capability to identify and “mark”various body parts in a series of 3D images. Applicants have realizedthat that system 200 may be modified to provide such capability.

Reference is now made to FIG. 7 which illustrates a novel system 300 foridentifying and tagging body parts in a 3D image. System 300 maycomprise 3D imaging system 100, object detector 110, anomaly detector120 and an object marker 180.

As in the previous embodiment, object detector 110 may receive depthmaps 50 from 3D imaging system 100 and may detect the objects therein.After processing depth maps 50, object detector 110 may forward theresulting object identified depth maps 51 to anomaly detector 120.Anomaly detector 120 may process depth maps 51 and may detect ananomalous gap as in the previous embodiment. Accordingly, as shown inanomaly identified depth map 52, anomaly detector 120 may identify a gapas specifically belonging to an object. For example, gap 265 may belongto a left arm.

In accordance with a preferred embodiment of the present invention,object marker 180 may receive anomaly identified depth maps 52 and may“mark” any identified anomalies as indicators for identified objects.For example, the size and shape of gap 265, as identified in FIG. 5, maybe saved as a marker 185 identifying an object. If the object is knownto be a left arm, then marker 185 identifies the arm as a left arm.

It will be appreciated that using marking gap 265 to mark an arm may beexemplary. Object marker 180 may be capable of using any anomalydetected by anomaly detector 120. For example, marker 180 may use ananomaly caused by highly reflective eyeglasses to mark a subject's eyesor head. Other highly reflective objects that may typically be used toidentify specific parts of a subject's body may include, for example,jewelry and clothing accessories such as rings, bracelets, anklets,brooches, earrings, necklaces, belt buckles, buttons, and snaps. Inaddition to eyeglasses, other medical artifacts such as prosthetics,walking canes and braces may also be sufficiently reflective to generateanomalies.

It will also be appreciated that the present invention also includesusing low reflective jewelry, medical artifacts, clothing and clothingaccessories to identify specific parts of a subject's body. Objectmarker 180 may use anomalous gaps caused by low reflective objects ingenerally the same manner as anomalous protrusions caused by highlyreflective objects.

It will further be appreciated that anomalies may even be caused by thedifferences in reflectivity of parts of a subject's body. For example,facial hair may tend to be less reflective than a subject's skin orclothing. Accordingly, a beard may be used to identify a subject's heador neck. Similarly, a mustache may be used to identify a subject'smouth, nose or head.

Markers 185 may be forwarded by object marker 180 to object detector110. Object detector 110 may use markers 185 as additional templates forthe identification of objects. It will be appreciated that once a marker185 is identified, object detector 110 may then reliably identify alarger object that may encompass marker 185. For example, objectdetector 110 may use gap 265 to identify a left arm. Similarly, objectdetector 110 may use marked anomalous protrusions caused by the lensesof eyeglasses to identify a subject's eyes or head.

It will also be appreciated that system 300 may forward marked images310 and/or markers 185 to other applications which may require generallyprecise identification of the objects in depth maps 50, 51, and 52.

In accordance with a preferred alternative embodiment of the presentinvention, users may undergo a calibration process when first usingsystem 300. Such a process may comprise displaying body parts and/orother objects as per a script or in response to prompting. In suchmanner, an inventory of markers 185 and the objects to which they areassociated may be acquired prior to operation of system 300 and may thusfacilitate smoother and more efficient operation.

In accordance with a preferred embodiment of the present invention,system 300 may also be used in conjunction with a multiplayer game. Insuch games, it may be necessary to differentiate between the playersbased on analysis of their images as represented in depth maps.Applicants have realized that markers 185 may also be used todifferentiate between two or more players.

A calibration process to identify and “mark” the players of such a gamemay be performed prior to the start of a multiplayer game. The playersmay be instructed to stand in specific poses such that their projectionsas perceived by system 100 may not overlap. Alternatively, they may beinstructed to pose separately. System 300 may then detect anomalies inthe individual players'projections as described hereinabove. Forexample, one player may have a non-reflecting beard and/or may bewearing highly reflective glasses. System 300 may mark such an anomalyas belonging to a specific player.

Such marked anomalies may be used as needed to distinguish between theplayers during the course of the game. For example, suppose that PlayerA may be wearing eyeglasses, whereas Player B may not. Since eyeglassestypically have high reflectivity, during calibration, they will be foundin IR images as two peaks with a relatively large area (compared to asimple eye glare). System 300 may detect and mark them as an anomalybelonging to Player A. While the game is played object detector 110detect similar peaks and distinguish them from other peaks ofreflectivity that might be found due to eye glare of players that do notwear glasses. Player A may thus be distinguished from other players thatmay not have eyeglasses.

It will be appreciated that in addition to multiplayer games thisembodiment may also include other applications with multipleparticipants.

It will also be appreciated that the use of marked anomalies as a meansto identify objects and/or players may be used instead of, or inaddition to, other means that may be available depending on thecircumstances and requirements of a specific application of the presentinvention. For example, an individual subject may also be identified byheight, size or some other feature or identifying characteristic. If forwhatever reason the use of these identifiers may be problematic, amarked anomaly may be used instead.

Unless specifically stated otherwise, as apparent from the precedingdiscussions, it is appreciated that, throughout the specification,discussions utilizing terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer, computing system, or similar electroniccomputing device that manipulates and/or transforms data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

Embodiments of the present invention may include apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the desired purposes, or it may comprise ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer.

The processes and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the desired method. The desired structure for avariety of these systems will appear from the description below. Inaddition, embodiments of the present invention are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the invention as described herein.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

What is claimed is:
 1. A computer-implemented method for processingimages, comprising: generating a depth map from at least one image;detecting at least one object in said depth map; and identifyinganomalies in said at least one object from said depth map, saididentifying anomalies comprises identifying at least one logical gapanomaly and at least one logical protrusion anomaly in said at least oneobject which are caused by differing reflective properties of the atleast one object, said identifying the at least one logical gap anomalycomprises identifying pixel depths which are less than those of pixelsin at least one adjacent region of said at least one object, and saididentifying the at least one logical protrusion anomaly comprisesidentifying pixel depths which are greater than those of pixels in atleast one adjacent region of said at least one object.
 2. Thecomputer-implemented method according to claim 1, wherein saididentifying anomalies comprises: calculating a depth difference bycomparing depths of at least two regions in said at least one object;and determining that said anomalies exist where an absolute value ofsaid depth difference exceeds a threshold.
 3. The computer-implementedmethod according to claim 1 wherein said at least one image is a firstimage generated using continuous radiation and a second image of thesame scene generated using pulsed radiation, and wherein saididentifying anomalies comprises: finding associated pixels of said firstand second images which have intensities below a threshold; calculatingdifferences between the intensities of said associated pixels in saidfirst and second images; and determining that the anomalies exist inresponse to determining that the calculated differences are less than apixel intensity threshold.
 4. The computer-implemented method accordingto claim 3, wherein said identifying anomalies comprises: derivingcompensated depth values by dividing the intensities of the first imageby the intensities of the second image; and determining if thecompensated depth values exceed a depth threshold, said determining thatsaid anomalies exist is responsive to the determining that thecompensated depth values exceed the depth threshold.
 5. Thecomputer-implemented method according to claim 1, further comprising:compensating for said anomalies by replacing pixels associated with saidanomalies with pixels of depth similar to that of at least one regionadjacent to said anomalies, wherein said region is a part of said atleast one object.
 6. The computer-implemented method according to claim1, wherein said depth map is in a series of depth maps, furthercomprising: marking said detected anomalies; and using said markedanomalies as a template, identifying future occurrences of said detectedat least one object in said series of depth maps.
 7. Acomputer-implemented method for processing images, comprising:identifying anomalies in an object in a depth map in a series of depthmaps, said identifying anomalies comprises identifying at least onelogical gap anomaly and at least one logical protrusion anomaly in saidobject which are caused by differing reflective properties of theobject, said identifying the at least one logical gap anomaly comprisesidentifying pixel depths which are less than those of pixels in at leastone adjacent region of said at least one object, and said identifyingthe at least one logical protrusion anomaly comprises identifying pixeldepths which are greater than those of pixels in at least one adjacentregion of said at least one object; and using said anomalies as atemplate, identifying future occurrences of said object in the series ofdepth maps.
 8. The computer-implemented method according to claim 7,wherein said anomalies are caused by materials with lower or higherreflective properties than those of other materials represented in saiddepth map.
 9. The computer-implemented method according to claim 7,further comprising performing said identifying anomalies as part of acalibration process prior to operation.
 10. The computer-implementedmethod according to claim 7, wherein said object is part of a subject'sbody.
 11. The computer-implemented method according to claim 10, whereinsaid anomalies are caused by a contrast in reflectivity between at leasttwo parts of said subject's body.
 12. The computer-implemented methodaccording to claim 7, wherein said using comprises: distinguishingbetween said object and a second similar object.
 13. Thecomputer-implemented method according to claim 12, wherein said objectand said similar object are a pair of objects, and one of said pair ofobjects is identified as a left object and one of said pair of objectsis a right object.
 14. The computer-implemented method according toclaim 7, further comprising: marking said object as a specificindividual.
 15. The computer-implemented method according to claim 14,wherein said individual is a player in a multiplayer game.
 16. Thecomputer-implemented method according to claim 7, further comprising:compensating for said anomalies by replacing pixels associated with saidanomalies with pixels of depth similar to that of at least one regionadjacent to said anomalies, wherein said at least one region is a partof said object.
 17. A computer-implemented method for processing images,comprising: generating a depth map from at least one image; detectingobjects within said depth map; and detecting anomalies in said detectedobjects, wherein said anomalies are caused by differing reflectiveproperties of the object, and comprise at least one logical gap anomalyand at least one logical protrusion anomaly in said depth map, thedetecting anomalies comprises detecting the at least one logical gapanomaly by identifying pixel depths which are less than those of pixelsin at least one adjacent region of said object, and detecting the atleast one logical protrusion anomaly by identifying pixel depths whichare greater than those of pixels in at least one adjacent region of saidobject.
 18. The computer-implemented method according to claim 17,wherein: the at least one image is obtained by processing imagesgenerated from both pulsed and continuous radiation; and said detectinganomalies comprises comparing pixel intensities of pixels fromassociated regions of said images, and responsive to said comparing,detecting anomalies when a depth of said pixels from said associatedregions of said images is closer than a threshold.
 19. Thecomputer-implemented method according to claim 17, wherein saiddetecting anomalies comprises comparing a difference in pixel depthbetween at least two regions of said detected objects.
 20. Thecomputer-implemented method according to claim 17, further comprising:generating a modified version of said depth map without said detectedanomalies.
 21. The computer-implemented method according to claim 17,further comprising: marking said detected anomalies and associating saiddetected anomalies with said detected objects.
 22. Thecomputer-implemented method according to claim 21, wherein saiddetecting objects comprises detecting and identifying said associatedobjects using said marked anomalies.
 23. The computer-implemented methodaccording to claim 21, wherein said marking comprises associating saiddetected anomalies as representing a specific individual subject. 24.The computer-implemented method according to claim 23, wherein saidindividual subject is a participant in a multiplayer application. 25.The computer-implemented method according to claim 21, wherein saiddetecting comprises using said marked anomalies, identifying anindividual subject in an application with multiple participants.
 26. Acomputer-implemented method for processing images, comprising: analyzingreflective properties of at least one object represented in a depth mapin a series of depth maps; detecting anomalies based on said analyzingby: identifying at least one logical gap anomaly and at least onelogical protrusion anomaly in said at least one object which are causedby differences in the reflective properties of said at least one object,said identifying the at least one logical gap anomaly comprisesidentifying pixel depths which are less than those of pixels in at leastone adjacent region of said at least one object, and said identifyingthe at least one logical protrusion anomaly comprises identifying pixeldepths which are greater than those of pixels in at least one adjacentregion of said at least one object; wherein said at least one object isat least one of a part of a subject's body and an object on saidsubject's body; marking said anomalies caused by differences in saidreflective properties; and using said marked anomalies as a template,identifying future occurrences of said at least one object in saidseries of depth maps.
 27. The computer-implemented method according toclaim 26, further comprising: using said marked anomalies,distinguishing between left and right paired objects, wherein said atleast one object is one of said paired objects.
 28. Thecomputer-implemented method according to claim 27, further comprising:using said marked anomalies, distinguishing between players of amultiplayer game.
 29. The computer-implemented method according to claim27, wherein said at least one object is at least one of: clothing, aclothing accessory, a part of said subject's body, jewelry and a medicalartifact.